Currently, with the development of networks such as the Internet and the development of computers, various methods and systems have been researched and developed for identifying a person in various situations such as providing services using a network, accessing another computer, or as an alternate to a physical key.
As a typical example of this kind of person identification, is a method (hereafter, ref erred to as an ‘operator recognition method’) that uses biological information about the operator such as spoken voice components or fingerprints, and more specifically, a characteristic amount of biological information such as spoken voice components or fingerprints of operators to be identified are registered in advance, and when identifying an operator, biological information that is input is extracted, and the extracted characteristic amount that is extracted is compared with the characteristic amount that was registered in advance to identify the operator.
For example, a method is known for identifying a person using the spoken voice component of an operator (hereafter, referred to as a speaker) that uses a probability model called HMM (Hidden Marcov Model) (hereafter, referred to as HMM). Particularly, recently a highly recognizable text dependent type recognition method is known that uses HMM data, in which the processing load of recognition is reduced by reducing the amount of HMM data that is registered in advance (hereafter referred to as HMM data).
More specifically, in this kind of text dependent operator recognition system (hereafter, referred to as a text dependent speaker recognition system) HMM for each speaker is calculated from a characteristic amount that is extracted from a plurality of times of speaking a word or phrase (hereafter, called the ‘password’) that is arbitrarily set for each operator, or in other words, speaker in advance, and registering the result as HMM data in a database, and when performing recognition for that speaker, speaker recognition is performed by having the speaker say the password and comparing a characteristic amount of that spoken voice component with the characteristic amount indicated by the HMM data (see Japanese patent application 2004-294755).